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SCIENTIA SINICA Informationis, Volume 50 , Issue 3 : 363-374(2020) https://doi.org/10.1360/SSI-2019-0196

An unmanned air combat system based on swarm intelligence

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  • ReceivedSep 10, 2019
  • AcceptedSep 21, 2019
  • PublishedFeb 27, 2020

Abstract

Unmanned aerial vehicles (UAVs) are usually controlled by radio or by autonomous control algorithms. Compared with manned aerial vehicles, they have great advantages in performing dangerous tasks but, presently, no UAV system can cope with high-intensity air combat. In addition, the robustness of a single UAV cannot be guaranteed in air combat missions; on the other hand, a multi-UAV system not only ensures this robustness but also improves the mission success rate by using saturation attacks. Therefore, this paper presents a multi-UAV air combat system based on swarm intelligence. Considering the problem of multi-UAV cooperative arrival at the air battlefield and the accomplishment of combat tasks, the aerodynamic model of the aircrafts and the threat area on the path to the battlefield are modeled; the path planning is completed through an ant colony algorithm. Based on the control algorithm of single-UAV finite-state machine and the cooperation of multiple UAVs, an autonomous control algorithm for multi-UAV systems is proposed to improve the success rate of UAV clusters in air combat. The effectiveness of the proposed algorithm is tested with a simulation platform.


References

[1] Duan H B, Qiu H X, Fan Y M. Unmanned aerial vehicle close formation cooperative control based on predatory escaping pigeon-inspired optimization. Sci Sin-Tech, 2015, 45: 559-572 CrossRef Google Scholar

[2] Duan H B, Luo Q N, Yu Y X.Trophallaxis network control approach to formation flight of multiple unmanned aerial vehicles.Sci China Tech Sci, 2013, 56: 1066--1074. Google Scholar

[3] Shen L C, Niu Y F, Zhu H Y. Theories and Methods of Autonomous Cooperative Control for Multiple UAVs. 2nd ed. Beijing: National Defense Industry Press, 2018. Google Scholar

[4] Schmitt F, Schulte A. Mixed-initiative mission planning using planning strategy models in military manned-unmanned teaming missions. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2015. 1391--1396. Google Scholar

[5] Lim Y, Ramasamy S, Gardi A. Cognitive Human-Machine Interfaces and Interactions for Unmanned Aircraft. J Intell Robot Syst, 2018, 91: 755-774 CrossRef Google Scholar

[6] Dai F, Chen M, Wei X L, et al. Swarm intelligence-inspired autonomous flocking control in UAV networks. IEEE Access, 2019. doi: 10.1109/ACCESS.2019.2916004. Google Scholar

[7] Yang J H, Kapolka M, Chung T H. Autonomy balancing in a manned-unmanned teaming (MUT) swarm attack. In: Robot Intelligence Technology and Applications. Berlin: Springer, 2013. 561--569. Google Scholar

[8] Duan H B, Shao S, Su B W, et al. New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle. Sci China Tech Sci, 2010, 53: 2025--2031. Google Scholar

[9] Huang H Q, Bai J Q, Zhou H, et al. Present situation and key technologies of unmanned cooperative operation under intelligent air combat system. Navigation Control, 2019, 18: 15--23. Google Scholar

[10] Yan F, Zhu X, Zhou Z. Real-time task allocation for a heterogeneous multi-UAV simultaneous attack. Sci Sin Inform, 2019, 49: 555-569 CrossRef Google Scholar

[11] Alfeo A L, Cimino M G C A, De Francesco N. Swarm coordination of mini-UAVs for target search using imperfect sensors. IDT, 2018, 12: 149-162 CrossRef Google Scholar

[12] Varela G, Caamamo P, Orjales F, et al. Swarm intelligence based approach for real time UAV team coordination in search operations. In: Proceedings of the 3rd World Congress on Nature and Biologically Inspired Computing, 2011. 365--370. Google Scholar

[13] Gao J S, Yu F, Ji X G. Current situation of studies on autonomous control level of UAVs. Electron Opt Control, 2009, 16: 51--54. Google Scholar

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